This changes Elasticsearch to automatically downgrade `text` and
`keyword` fields into appropriate `string` fields when changing the
mapping of indexes imported from 2.x. This allows users to use the
modern, documented syntax against 2.x indexes. It also makes it clear
that reindexing in order to recreate the index in 5.0 is required for
any long lived indexes. This change is useful for the times when you
can't (cluster is just starting, not stable enough for reindex) or
shouldn't (index will only live 90 days or something).
Fix field examples to make documents actually visible
This commit adds refresh calls to field examples an removes not working
`_routing` and `_field_names` script access.
Closes#20118
This includes:
- All regular numeric types such as int, long, scaled-float, double, etc
- IP addresses
- Dates
- Geopoints and Geoshapes
Relates to #19784
Adds `warnings` syntax to the yaml test that allows you to expect
a `Warning` header that looks like:
```
- do:
warnings:
- '[index] is deprecated'
- quotes are not required because yaml
- but this argument is always a list, never a single string
- no matter how many warnings you expect
get:
index: test
type: test
id: 1
```
These are accessible from the docs with:
```
// TEST[warning:some warning]
```
This should help to force you to update the docs if you deprecate
something. You *must* add the warnings marker to the docs or the build
will fail. While you are there you *should* update the docs to add
deprecation warnings visible in the rendered results.
This is a tentative to revive #15939 motivated by elastic/beats#1941.
Half-floats are a pretty bad option for storing percentages. They would likely
require 2 bytes all the time while they don't need more than one byte.
So this PR exposes a new `scaled_float` type that requires a `scaling_factor`
and internally indexes `value*scaling_factor` in a long field. Compared to the
original PR it exposes a lower-level API so that the trade-offs are clearer and
avoids any reference to fixed precision that might imply that this type is more
accurate (actually it is *less* accurate).
In addition to being more space-efficient for some use-cases that beats is
interested in, this is also faster that `half_float` unless we can improve the
efficiency of decoding half-float bits (which is currently done using software)
or until Java gets first-class support for half-floats.
If there are percolator queries containing `range` queries with ranges based on the current time then this can lead to incorrect results if the `percolate` query gets cached. These ranges are changing each time the `percolate` query gets executed and if this query gets cached then the results will be based on how the range was at the time when the `percolate` query got cached.
The ExtractQueryTermsService has been renamed `QueryAnalyzer` and now only deals with analyzing the query (extracting terms and deciding if the entire query is a verified match) . The `PercolatorFieldMapper` is responsible for adding the right fields based on the analysis the `QueryAnalyzer` has performed, because this is highly dependent on the field mappings. Also the `PercolatorFieldMapper` is responsible for creating the percolate query.
Rename `fields` to `stored_fields` and add `docvalue_fields`
`stored_fields` parameter will no longer try to retrieve fields from the _source but will only return stored fields.
`fields` will throw an exception if the user uses it.
Add `docvalue_fields` as an adjunct to `fielddata_fields` which is deprecated. `docvalue_fields` will try to load the value from the docvalue and fallback to fielddata cache if docvalues are not enabled on that field.
Closes#18943
`stored_fields` parameter will no longer try to retrieve fields from the _source but will only return stored fields.
`fields` will throw an exception if the user uses it.
Add `docvalue_fields` as an adjunct to `fielddata_fields` which is deprecated. `docvalue_fields` will try to load the value from the docvalue and fallback to fielddata cache if docvalues are not enabled on that field.
Closes#18943
They have been implemented in https://issues.apache.org/jira/browse/LUCENE-7289.
Ranges are implemented so that the accuracy loss only occurs at index time,
which means that if you are searching for values between A and B, the query will
match exactly all documents whose value rounded to the closest half-float point
is between A and B.
`doc_values` for _type field are created but any attempt to load them throws an IAE.
This PR re-enables `doc_values` loading for _type, it also enables `fielddata` loading for indices created between 2.0 and 2.1 since doc_values were disabled during that period.
It also restores the old docs that gives example on how to sort or aggregate on _type field.
Remove the arbitrary limit on epoch_millis and epoch_seconds of 13 and 10
characters, respectively. Instead allow any character combination that can
be converted to a Java Long.
Update the docs to reflect this change.
* Docs: First pass at improving analyzer docs
I've rewritten the intro to analyzers plus the docs
for all analyzers to provide working examples.
I've also removed:
* analyzer aliases (see #18244)
* analyzer versions (see #18267)
* snowball analyzer (see #8690)
Next steps will be tokenizers, token filters, char filters
* Fixed two typos
Adds infrastructure so `gradle :docs:check` will extract tests from
snippets in the documentation and execute the tests. This is included
in `gradle check` so it should happen on CI and during a normal build.
By default each `// AUTOSENSE` snippet creates a unique REST test. These
tests are executed in a random order and the cluster is wiped between
each one. If multiple snippets chain together into a test you can annotate
all snippets after the first with `// TEST[continued]` to have the
generated tests for both snippets joined.
Snippets marked as `// TESTRESPONSE` are checked against the response
of the last action.
See docs/README.asciidoc for lots more.
Closes#12583. That issue is about catching bugs in the docs during build.
This catches *some* bugs in the docs during build which is a good start.
* Added an extra `field` parameter to the `percolator` query to indicate what percolator field should be used. This must be an existing field in the mapping of type `percolator`.
* The `.percolator` type is now forbidden. (just like any type that starts with a `.`)
This only applies for new indices created on 5.0 and later. Indices created on previous versions the .percolator type is still allowed to exist.
The new `percolator` field type isn't active in such indices and the `PercolatorQueryCache` knows how to load queries from these legacy indices.
The `PercolatorQueryBuilder` will not enforce that the `field` parameter is of type `percolator`.
This makes all numeric fields including `date`, `ip` and `token_count` use
points instead of the inverted index as a lookup structure. This is expected
to perform worse for exact queries, but faster for range queries. It also
requires less storage.
Notes about how the change works:
- Numeric mappers have been split into a legacy version that is essentially
the current mapper, and a new version that uses points, eg.
LegacyDateFieldMapper and DateFieldMapper.
- Since new and old fields have the same names, the decision about which one
to use is made based on the index creation version.
- If you try to force using a legacy field on a new index or a field that uses
points on an old index, you will get an exception.
- IP addresses now support IPv6 via Lucene's InetAddressPoint and store them
in SORTED_SET doc values using the same encoding (fixed length of 16 bytes
and sortable).
- The internal MappedFieldType that is stored by the new mappers does not have
any of the points-related properties set. Instead, it keeps setting the index
options when parsing the `index` property of mappings and does
`if (fieldType.indexOptions() != IndexOptions.NONE) { // add point field }`
when parsing documents.
Known issues that won't fix:
- You can't use numeric fields in significant terms aggregations anymore since
this requires document frequencies, which points do not record.
- Term queries on numeric fields will now return constant scores instead of
giving better scores to the rare values.
Known issues that we could work around (in follow-up PRs, this one is too large
already):
- Range queries on `ip` addresses only work if both the lower and upper bounds
are inclusive (exclusive bounds are not exposed in Lucene). We could either
decide to implement it, or drop range support entirely and tell users to
query subnets using the CIDR notation instead.
- Since IP addresses now use a different representation for doc values,
aggregations will fail when running a terms aggregation on an ip field on a
list of indices that contains both pre-5.0 and 5.0 indices.
- The ip range aggregation does not work on the new ip field. We need to either
implement range aggs for SORTED_SET doc values or drop support for ip ranges
and tell users to use filters instead. #17700Closes#16751Closes#17007Closes#11513
Nik Everett
Munish Goyal
Lee Hinman
Nicholas Knize
Simon Willnauer
Jim Ferenczi
David Pilato
Adrien Grand
Clinton Gormley
Martijn van Groningen
Britta Weber
Robert Muir
Robert Muir
G. Richard Bellamy
Robin Joseph
Shane Connelly
Christoph Büscher
LeonardGC